Clinical and economic impact of the availability of innovative therapies for advanced lung cancer in men in Latin America: a population-based secondary data study
Bibliographic record
Abstract
Background: Over the last decade, the development of innovative cancer treatments has accelerated and has been associated with improved mortality trends; however, local regulatory approval times are extensive. This study estimated the clinical and economic impact of delays in the approval of innovative therapies for the treatment of advanced lung cancer in men in five Latin American countries. Methods: Using public data, we estimated the relationship between available innovative therapies (AIT) and age-specific mortality rate (ASMR) for Argentina, Brazil, Chile, Colombia, and Mexico through a regression model. Based on the difference between the number of FDA-approved therapies and the number approved by each local agency, we calculated the avoidable deaths (ADs) if innovation had been available. We estimated the Years of Life Lost (YLLs) using the life expectancy, the median age of death, and the ADs. Productivity loss (PL) was calculated using each country's retirement age and yearly Gross Domestic Product per capita (GDPc) in 2022 constant USD. Findings: Total ADs, YLLs, and PL were 8694, 114,477, and USD 439,179,876, respectively. Argentina had the highest impact of AIT on ASMR. Brazil's results showed a high clinical and economic impact, primarily due to its large population. Chile's high GDPc led to high PL. Colombia and Mexico showed a high clinical impact, suggesting a benefit of early approval. Differences in availability and approval times have increased with the number of FDA-approved therapies, yet local time gaps have recently increased. Interpretation: Our study shows the substantial clinical and economic impact of delays in approving innovative therapies, underscoring the potential of improving regulatory processes to increase the availability of lung cancer treatments. Accelerating the introduction of innovative therapies for advanced lung cancer in Latin America represents a significant opportunity to enhance survival rates, instilling hope and optimism while also avoiding substantial PL. Funding: This study was conducted as a research partnership between Roche and CTIC. No funding was received. Authors participated in the study design, data collection, data analysis, interpretation, and writing of the report.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".